---
title: "Supervised Local Tangent Space Alignment for Classification"
authors:
  - "Hongyu Li"
  - "Wenbin Chen"
  - "I-Fan Shen"
doi: ""
url: "https://ijcai.org/proceedings/2005/343"
pdf: "https://ijcai.org/proceedings/2005/0343.pdf"
venue: "Proceedings of the Nineteenth International Joint Conference on Artificial Intelligence"
year: 2005
pages: "1620-1620"
track: ""
keywords:
---

# Supervised Local Tangent Space Alignment for Classification

Hongyu Li, Wenbin Chen, I-Fan Shen

*Proceedings of the Nineteenth International Joint Conference on Artificial Intelligence*, pages 1620-1620.

## Abstract

Supervised local tangent space alignment (SLTSA) is an extension of local tangent space alignment (LTSA) to supervised feature extraction. Two algorithmic improvements are made upon LTSA for classification. First a simple technique is proposed to map new data to the embedded low-dimensional space and make LTSA suitable in a changing, dynamic environment. Then SLTSA is introduced to deal with data sets containing multiple classes with class membership information.